Applying Deep Learning to Ads Conversion Prediction in Last Mile Delivery Marketplace
This work addresses ads ranking for DoorDash, but it is incremental as it applies existing deep learning methods to a specific domain.
The paper tackles the problem of improving ads conversion prediction in a last-mile delivery marketplace by transitioning from traditional tree-based models to multi-task deep neural networks, resulting in substantial business impact and reshaping their machine learning approach.
Deep neural networks (DNNs) have revolutionized web-scale ranking systems, enabling breakthroughs in capturing complex user behaviors and driving performance gains. At DoorDash, we first harnessed this transformative power by transitioning our homepage Ads ranking system from traditional tree based models to cutting edge multi task DNNs. This evolution sparked advancements in data foundations, model design, training efficiency, evaluation rigor, and online serving, delivering substantial business impact and reshaping our approach to machine learning. In this paper, we talk about our problem driven journey, from identifying the right problems and crafting targeted solutions to overcoming the complexity of developing and scaling a deep learning recommendation system. Through our successes and learned lessons, we aim to share insights and practical guidance to teams pursuing similar advancements in machine learning systems.